Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
How to Form Winning Coalitions in Mixed Human-Computer Settings
Authors: Yair Zick, Kobi Gal, Yoram Bachrach, Moshe Mash
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using this platform, we collected hundreds of instances of users negotiation dynamics, the coalitions they formed, and the way revenue was shared. We designed a negotiating software agent and tested its performance when interacting with other people playing this game. We compare several predictive models using the above features, varying the type of power index used (Banzhaf, Shapley-Shubik, Banzhaf, Deegan-Packel, Extended Deegan-Packel). For each power index configuration, we implement several supervised machine learning models: logistic regression, a multilayer neural network (3 hidden layers, 3 decision nodes in each layer), and a Naive Bayes model. We report the receiver-operator characteristic curve (AUC)... |
| Researcher Affiliation | Collaboration | Moshe Mash Ben-Gurion University EMAIL Yoram Bachrach Digital Genius EMAIL Ya akov (Kobi) Gal Ben-Gurion University EMAIL Yair Zick National University of Singapore EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | In the spirit of public repositories in computational social choice [Mattei and Walsh, 2013; Tal et al., 2015], we are making our platform open source, and have created a public library which will include all of the collected data, and made freely available to the research community at https://tinyurl.com/mrna7w6. |
| Open Datasets | Yes | Using this platform, we collected hundreds of instances of users negotiation dynamics, the coalitions they formed, and the way revenue was shared. we are making our platform open source, and have created a public library which will include all of the collected data, and made freely available to the research community at https://tinyurl.com/mrna7w6. |
| Dataset Splits | Yes | Table 1 describes the AUC score the logistic regression for the different indices using ten-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments or training models. |
| Software Dependencies | No | The paper mentions machine learning models used but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Finding an approximately optimal x is done by iterating over all possible payoff divisions in 5 unit intervals. All subjects played a 5-agent configuration of the cooperative negotiation game, in which agent weights varied between 1 and 9, the threshold t was set to 10, and the coalition value r was set to 100. The maximal number of rounds was set to 3 for all games. |